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fixes : speculative KV cache + llama worst-case graph

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Georgi Gerganov 2023-09-18 22:00:02 +03:00
parent 466b513851
commit 897caccdf4
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GPG Key ID: 449E073F9DC10735
3 changed files with 12 additions and 19 deletions
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20
examples/parallel/parallel.cpp
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@ -80,7 +80,7 @@ int main(int argc, char ** argv) {
return 1;
}
const int n_clients = 16;
const int n_clients = 4;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("parallel", "log"));
@ -116,10 +116,6 @@ int main(int argc, char ** argv) {
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
auto t_main_start = ggml_time_us();
int64_t n_tokens_total = 0;
llama_seq_id g_seq_id = 0;
std::vector<llama_token> batch_token;
@ -203,6 +199,9 @@ int main(int argc, char ** argv) {
continue;
}
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
const llama_token id = llama_sample_token(ctx, NULL, NULL, params, client.last_tokens, candidates, client.i_batch - i);
if (client.t_start_gen == 0) {
@ -233,9 +232,7 @@ int main(int argc, char ** argv) {
const auto t_main_end = ggml_time_us();
n_tokens_total += client.n_decoded - client.n_prompt;
printf("\033[1mClient %2d, seq %4d, prompt %4d t, response %4d t, speed: PP %5.2f t/s, TG %5.2f, AVG %5.2f \033[0m: \n\nInput: %s\nResponse: %s\n\n",
printf("\033[1mClient %2d, seq %4d, prompt %4d t, response %4d t, speed: PP %5.2f t/s, TG %5.2f t/s, AVG %5.2f t/s \033[0m: \n\nInput: %s\nResponse: %s\n\n",
client.id, client.seq_id, client.n_prompt, client.n_decoded - client.n_prompt,
(double) (client.n_prompt ) / (client.t_start_gen - client.t_start_prompt) * 1e6,
(double) (client.n_decoded - client.n_prompt) / (t_main_end - client.t_start_gen) * 1e6,
@ -249,13 +246,6 @@ int main(int argc, char ** argv) {
client.i_batch = -1;
}
}
static bool is_first = true;
if (is_first) {
t_main_start = ggml_time_us();
n_tokens_total = 0;
is_first = false;
}
}
LOG_TEE("\n\n");

3
examples/speculative/speculative.cpp
View File

@ -172,6 +172,7 @@ int main(int argc, char ** argv) {
LOG("out of drafted tokens\n");
}
llama_kv_cache_rm_seq(ctx_dft, 0, n_past_dft, n_ctx);
llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0), params.n_threads);
++n_past_dft;
@ -256,6 +257,7 @@ int main(int argc, char ** argv) {
}
// evaluate the drafted token on the draft model
llama_kv_cache_rm_seq(ctx_dft, 0, n_past_cur, n_ctx);
llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0), params.n_threads);
++n_past_cur;
@ -265,6 +267,7 @@ int main(int argc, char ** argv) {
}
// evaluate the target model on the drafted tokens
llama_kv_cache_rm_seq(ctx_tgt, 0, n_past_tgt, n_ctx);
llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0), params.n_threads);
++n_past_tgt;

8
llama.cpp
View File

@ -2604,7 +2604,7 @@ static struct ggml_cgraph * llm_build_llama(
const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = llama_kv_cache_cell_max(kv_self);
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : llama_kv_cache_cell_max(kv_self);
//printf("n_kv = %d\n", n_kv);
@ -2775,7 +2775,7 @@ static struct ggml_cgraph * llm_build_llama(
offload_func_kq(Kcur);
ggml_set_name(Kcur, "Kcur");
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
offload_func_kq(Qcur);
ggml_set_name(Qcur, "Qcur");
@ -6677,9 +6677,9 @@ struct llama_context * llama_new_context_with_model(
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
// build worst-case graph
uint32_t n_tokens = std::max((int)hparams.n_ctx, params.n_batch);
const uint32_t n_tokens = std::min((int) hparams.n_ctx, params.n_batch);
llama_token token = llama_token_bos(ctx); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, 0, 0));
ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, hparams.n_ctx - n_tokens, 0));
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {